We propose to model the persistent-transient duality in human behavior using a parent-child multi-channel neural network, which features a parent persistent channel that manages the global dynamics and children transient channels that are initiated and terminated on-demand to handle detailed interactive actions. The short-lived transient sessions are managed by a proposed Transient Switch. The neural framework is trained to discover the structure of the duality automatically. Our model shows superior performances in human-object interaction motion prediction.
翻译:我们提议使用母子多通道神经网络模拟人类行为中持久、短暂的双重性,其特点是母子多道神经网络,其特点是管理全球动态的母体持续频道,儿童随需要启动和终止的瞬间频道,以便处理详细的交互行动。短寿命短暂的瞬间会话由拟议的瞬时开关管理。神经框架经过培训,可以自动发现双重性的结构。我们的模型显示人类物体互动运动预测的优异性能。